{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 需求\n",
    "\n",
    "- **任务** 根据花的特征预测其类别（以鸢尾花数据集为例）\n",
    "\n",
    "- **数据** 鸢尾花（Iris）数据集，是机器学习入门常用的数据集，包含150个样本，4个特征和3个类别。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、获取数据与分析：\n",
    "\n",
    "**数据集：**Python中的**sklearn**库自带的Iris数据集\n",
    "\n",
    "- 4个特征：`sepal length`, `sepal width`, `petal length`, `petal width`\n",
    "\n",
    "- 目标：3个类别`setosa`, `versicolor`, `virginica`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 150 (50 in each of three classes)\\n:Number of Attributes: 4 numeric, predictive attributes and the class\\n:Attribute Information:\\n    - sepal length in cm\\n    - sepal width in cm\\n    - petal length in cm\\n    - petal width in cm\\n    - class:\\n            - Iris-Setosa\\n            - Iris-Versicolour\\n            - Iris-Virginica\\n\\n:Summary Statistics:\\n\\n============== ==== ==== ======= ===== ====================\\n                Min  Max   Mean    SD   Class Correlation\\n============== ==== ==== ======= ===== ====================\\nsepal length:   4.3  7.9   5.84   0.83    0.7826\\nsepal width:    2.0  4.4   3.05   0.43   -0.4194\\npetal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\npetal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n============== ==== ==== ======= ===== ====================\\n\\n:Missing Attribute Values: None\\n:Class Distribution: 33.3% for each of 3 classes.\\n:Creator: R.A. Fisher\\n:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n:Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n  Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n  Mathematical Statistics\" (John Wiley, NY, 1950).\\n- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n  (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n- Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n  Structure and Classification Rule for Recognition in Partially Exposed\\n  Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n  Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n- Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n  on Information Theory, May 1972, 431-433.\\n- See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n  conceptual clustering system finds 3 classes in the data.\\n- Many, many more ...\\n\\n|details-end|\\n',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 为matplotlib添加中文字体\n",
    "plt.rcParams['axes.unicode_minus']=False #正常显示负数\n",
    "\n",
    "# 加载数据\n",
    "iris = load_iris()\n",
    "iris\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                  5.1               3.5                1.4               0.2   \n",
      "1                  4.9               3.0                1.4               0.2   \n",
      "2                  4.7               3.2                1.3               0.2   \n",
      "3                  4.6               3.1                1.5               0.2   \n",
      "4                  5.0               3.6                1.4               0.2   \n",
      "..                 ...               ...                ...               ...   \n",
      "145                6.7               3.0                5.2               2.3   \n",
      "146                6.3               2.5                5.0               1.9   \n",
      "147                6.5               3.0                5.2               2.0   \n",
      "148                6.2               3.4                5.4               2.3   \n",
      "149                5.9               3.0                5.1               1.8   \n",
      "\n",
      "     target  \n",
      "0         0  \n",
      "1         0  \n",
      "2         0  \n",
      "3         0  \n",
      "4         0  \n",
      "..      ...  \n",
      "145       2  \n",
      "146       2  \n",
      "147       2  \n",
      "148       2  \n",
      "149       2  \n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    },
    {
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d2q5u+RVK+wodkNMhPZHetLPpYmr4Fc99X3FZdCs56ut0yQsZDYUUU9N/wybDUIJTWj66sMVbfjmcDl3y+iVNTqq72pBJCdYELb9mebu6/ZO/1ssv7YTYv4Vw3edoP9prH7g9Y8wEACEmxPqHHtUdkP6ReOS4B8oacv17QtOCx6FMFukvZQ3/H4hYs8W7bXvPBunJX3m3D6T2s78AhAVf+sCtOlM9ZMgQ/fTTT5KkKVOm6KyzzlJmZqZOPPFEXXbZZfrkk0+0d+/e1jSNwxQUV7Z4cl6SDEmlVTUqKK5sN7m8uHabx0KK1HCFyotrt/meZBCF0r5CB1SyxkNnU5IMyb6zIQ6tUvjViw239mqmkCJJhsmkMotJhV+92HIb5YUtnlSXJEOGyqrLVFheeNT5tiV/rZdf2gmxfwvhus8BHBljJgAIESHWP/RoxSTv49bN9lzwkCTD0RAXqFhvt+262d7vg/a0vwB0OF49M6XRvn37dP755+uyyy5TUlLDsx/uvfdet5jGC13WrVunwYMH+ynNjqt8X8sn51sTdzT8lUtJZbVX7XgbFypCaV+hA9rf8snaVsWhiQr7D0cdV1Fd4V0bXsaFCn+tl1/aCbF/C+G6zwG0jDETAISYEOsfelS51fs4k5e/j96zzfvl+xrbNcG/7fqyD0JhfwHocHwqpsyaNUuGYbgGBc0xmUxavXq1zj777KNODlJ8t2i/xh0Nf+WS0sPqVTvexoWKUNpX6IC87cR6G4cm4mKOP+q4OGucd214GRcq/LVefmknxP4thOs+B9AyxkwAEGJCrH/oUY8TGh42701c997eteltXGtivd1m3rbryz4Ihf0FoMPx6TZfw4YN01VXXXXETj+DAv/JTO2hJFu0WnrChklSki1amak92k0uowb21pEeGWI2NcS1J6G0r9ABpZzTcN9YT9/AmOMa4tAqGaeOUoLDkKmFR42ZDEOJDkMZp45quY34DCVYE2RqYT+ZZFKiNVEZ8Rl+ybmt+Gu9/NJOiP1bCNd9DqBljJkAIMSEWP/Qo8EPex834LYjX51isjTEBSrW22074Dbv90F72l8AOpxWPTPlj3/8o7/zQAssZpOmDEmT1PTPSOP0lCFpbfJAc3/lEhVh1m3npXqMue281Hb18HkptPYVOiCzRcqe8b+JFr6B2dN5QN9RsEREKbfvTZLUpKDSOD2h700tPnxekixmi3Izcxs+c9h+apyekDmh3T2I3F/r5Zd2QuzfQrjucwAtq6ur08CBA7Vy5UpJ0mWXXeaaP3To0GCmBgAdU4j1Dz2K6iz1u8xzTL/LGuIioqSBd3uOHfj7hrhAxXq7bSOivN8H7Wl/AehwWnW2+uWXX/Z3HvAgOz1Js0ZmKNHmfnuoRFu0Zo3MUHZ6y7cQCNVcJl6WpjvOT21yhYrZJN1xfqomXpbmr5TbVCjtK3RAaUOl61+QYg77nsUc2zA/jRM4Ryvr3InK63OT4g97HmOCU8rrc5Oyzp145DZSspQ3KE/x1nj3NqwJyhuUp6yULH+m3Gb8tV5+aSfE/i2E6z4H0FRhYaFWr14tk8mkOXPmKCMjQ19//bXeeOMNvfXWW/ruu+/0xhtv6I033mBMBQBtKcT6hx7d8HLLBZV+lzW83+jih6Rz7ml6JYnJ0jD/4ocCH+vttvVlH7Sn/QWgQzEZRgv3K/HgxBNP1Pfff++a/te//qXIyEiZzWbV19fr4MGDTR6yGGx2u102m01VVVWKiYkJdjqt4nAaKiiuVPm+GsV3a7hdVLCucvBXLnX1Tr24dptKKquV0sOqUQN7t7srUpoTSvsKHZDTIZWsaXggX9eEhsuf+dWOXznq61T41YuqsP+guJjjlXHqKI9XpDTbhtOhwvJCVVRXKM4ap4z4jLC4OsFf6+WXdkLs30K47nOEtnDoA7cX27dv1+DBg/Xiiy9q/PjxWrJkiZYtW6Zp06bp4osvltTwo7Qbb7xRTqdTdXV1evLJJ4OctTu+LwDCXoj1Dz2qOyCtmNTwsPkeJzTc2iuqc/Ox9XXSutkND3rv3rvhtlotjU8CFevttvVlH7Sn/QWg3fKlD+x1MeXVV19VVFSUzGaz7rnnHm3bts31XnR0tLKzsyVJTqdT9fX1euedd1q/BgHAwAAAAAAdDX3gtmW329WlSxede+656tmzp7KzszV//nytXbtWDodD9957r0aOHKk//vGP+te//qXTTz892Cm74fsCAACAjsaXPnCEt40++OCD6tSpk0wmk6qrq93e69atm958881WJQsAAAAA4eDLL7/UJ598oosuukiDBw9WXV2d0tPT9dxzz+nLL7/Uxo0bVVVVpRtvvFGnnXZasNMFAAAA4AOv76f07bffauPGjdqwYYNsNpvbeyYTty8CAAAA0LGZzWa98sor6tmzp7788ktt3bpV559/vnr16qU//OEPWr16tU466SQ99dRT2r9/f7DTBQAAAOADr69MORTFEwAAAABwd8455+jgwYP64osvXPMOv6uy0+nU7t27tXPnziY/UgMAAAAQulpVTAEAAAAANNWvXz/17NlThmHIZDLJbDYrMjJSPXr00CmnnKLTTz9dZWVlSk5ODnaqAAAAAHzg9W2+AAAAAACejR07Vt26dVNycrLGjBmjvXv36tprr5UkvfDCC7rjjjuUk5Ojzz//PMiZAgAAAPCF11emDB06VOvWrVNkZKRKS0uVmpqqiIgIRUREqKqqSvfcc4+uv/56nXvuuYHMFwAAAABCkmEYioqK0p49e1RUVKQ33nhD5eXlevrpp7V161bV1tZq0qRJ+uabb1RVVRXsdAEAAAD4wOtiysMPP6wDBw4oMjJSZrNZP//8s6qqqrRnzx5t3rxZq1at0tNPP60zzzxTs2bN0umnnx7IvAEAAAAgZOzevVuXXXaZzjnnHHXt2lWvvPKK3nvvPd1///0699xztXfvXu3fv19LlizRTTfdpLvuuivYKQMAAADwgdfFlNNOO+2IMd9//70mTpyoe+65R6tXrz6qxAAAAACgvejZs6deeOEFVVZW6s0339To0aOVnp6uhIQEffjhh3r99ddVVFSkO+64QzNnztSFF17ImAkAAABoR/zyAHq73a6IiAideOKJevXVV/Xjjz/6o1kAAAAAaDdOPvlklZaW6tprr1V6erqsVqv69+8vSTr77LOVnp6u+Ph4/fWvf1VpaWmQswUAAADgC68fQF9XV6eLLrpIklRZWamqqiodPHhQkjRnzhwtW7bMFXvsscf6OU0AAAAACH2nn366MjMzZbVaJUkDBw6UJB133HHq16+fPv30U8XHx2vp0qXBTBMAAACAj7wupkRFRenAgQOSGoolJ510kpYsWaLXX39dUVFRioyM1EcffaQNGzYEKlcAAAAACGnR0dGu/1+xYoXOO+88/fzzz655Z511lt544w09/fTTwUgPAAAAQCv5dJsvk8kkSTr11FN19913y+FwaPr06brjjjtksVg0fvx4FRcX64QTTlBBQUFAEkZocDgNFRRXqnxfjeK7RSsztYcsZlO7z6eu3qkX125TSWW1UnpYNWpgb0VFeF1zBNBBOJwOFZYXqqK6QnHWOGXEZ8hitgQ7raPiqDugwtUPqaKqRHG2FGWcN1mWqM5By6eu7oAWfjZD2+0//P/27j0+ivLu+/h3syEJAXYxSkI4GKJgNSKtUSLwCB4aNVVA9OmteCNWbS1i1SrWcrhFS2mF9KaR2lawPB4R6+muFSyiUlGxHAKNoNyhWjVEwECC4G5iSDbZneePhZWQ7LAb9jC7+bxfr321c+U31/7m2stlZn47MxroOFnXnjddaXHMBwBClZLyzb7jmDFjdNZZZ+naa68NXIlis9l06aWXyuPxxCtFAEC8+LxS9TqpYa/UM0fKGyUFO44IJzZaOVih30R5fwBdQsjFlLq6Onm93sBOv81mC1yRcpjNZlNtba1OP/10ffzxxzrttNMinzHibtW2Gs1ZUakaV1OgLdeZoQfGFahkaG7C5jNvZaWWrK2Sz/im7dcrt+uW0fmaeXlBJFMGkMBWV6/W/PL52tu4N9CWk5mjGUUzVJxXHMfMOm/1Kz/U/H3rtDf10G5B/QfKWfqyZpw0SsVXPhbzfMpW3aan9rwr36EfcWjfJi349CX9oO8YTSvhl9wArKm1tVXbt29XS0uLtm/frvz8fGVkZOi5557TFVdcodLSUk2fPj0Qf/iHagCALqJyubRquuQ+4jnDjn5SSalUML7zsdHKwQr9Jsr7A+gyQvrJvdvt1mmnnaatW7fq97//vT7//HM988wzktofBKSkpKhv375yuVyRzxZxt2pbjaY+U9GmcCFJe1xNmvpMhVZti+2DNCOVz7yVlXr03baFFEnyGdKj71Zp3srKSKUMIIGtrl6taW9Pa1NIkaTaxlpNe3uaVlevjlNmnbf6lR9q2oGN2mtv+6utWrtd0w5s1OpXfhjTfMpW3aYn9rwr31HtPklP7HlXZatui2k+ABCqffv26eKLL1ZNTY0uuOACbdq0SbNmzdKcOXOUl5enBx54QDNnztT999+vH//4x+renavtAKDLqFwuvXBD25P9kuSu8bdXLu9cbLRysEK/ifL+ALqUkIopDodDBw4c0Nlnn6177rlH//3f/60bb7xRhYWFHca/+eabGj58eEQTRfx5fYbmrKiU0cHfDrfNWVEp79EVCYvn42n1acnaKtOYJWur5Gk9+tQegK7E6/Nqfvl8GR186xxuKy0vldfnjXVqneb1HNT8fev82R/14wjj0HLpvnXyeg7GJB+P56Ce2vOuf+HoX2wfWn5qz7vyxCgfAAhH3759VVdXp4EDB6q2tlbDhg1Ta2urDMNQdna2Jk6cqD/+8Y+SpJycHD300ENxzhgAEBM+r/+qCbOzF6tm+OPCiY1WDlboN1HeH0CXE9bDIA5fhVJdXa3q6mo1NjZ2GJeenn78mcFyyqv2t7sC5EiGpBpXk8qr9idUPkvX72h3Rcr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      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "y = pd.Series(iris.target)\n",
    "# 深拷贝X\n",
    "X_plt = X.copy()\n",
    "X_plt[\"target\"] = iris.target\n",
    "# 数据概览\n",
    "print(X_plt)\n",
    "# 将数据按照target列分组，将target相同的数据放在一起\n",
    "grouped = X_plt.groupby('target')\n",
    "# 绘制散点图\n",
    "plt.figure(figsize=(20,8))\n",
    "# 绘制散点图\n",
    "plt.subplot(1,2,1)\n",
    "for name, group in grouped:\n",
    "\tplt.scatter(group.iloc[:, 0], group.iloc[:, 1], label=iris.target_names[name])\n",
    "plt.xlabel('萼片长度')\n",
    "plt.ylabel('萼片宽度')\n",
    "plt.title('萼片长度和宽度的散点图')\n",
    "plt.legend()\n",
    "# 绘制散点图\n",
    "plt.subplot(1,2,2)\n",
    "for name, group in grouped:\n",
    "\tplt.scatter(group.iloc[:, 2], group.iloc[:, 3], label=iris.target_names[name])\n",
    "plt.xlabel('花瓣长度')\n",
    "plt.ylabel('花瓣宽度')\n",
    "plt.title('花瓣长度和宽度的散点图')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据预处理\n",
    "\n",
    "- 数据标准化：将特征进行归一化或标准化。\n",
    "- 划分训练集和测试集（80%训练，20%测试）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.47393679,  1.20365799, -1.56253475, -1.31260282],\n",
       "       [-0.13307079,  2.99237573, -1.27600637, -1.04563275],\n",
       "       [ 1.08589829,  0.08570939,  0.38585821,  0.28921757],\n",
       "       [-1.23014297,  0.75647855, -1.2187007 , -1.31260282],\n",
       "       [-1.7177306 ,  0.30929911, -1.39061772, -1.31260282],\n",
       "       [ 0.59831066, -1.25582892,  0.72969227,  0.95664273],\n",
       "       [ 0.72020757,  0.30929911,  0.44316389,  0.4227026 ],\n",
       "       [-0.74255534,  0.98006827, -1.27600637, -1.31260282],\n",
       "       [-0.98634915,  1.20365799, -1.33331205, -1.31260282],\n",
       "       [-0.74255534,  2.32160658, -1.27600637, -1.44608785],\n",
       "       [-0.01117388, -0.80864948,  0.78699794,  0.95664273],\n",
       "       [ 0.23261993,  0.75647855,  0.44316389,  0.55618763],\n",
       "       [ 1.08589829,  0.08570939,  0.55777524,  0.4227026 ],\n",
       "       [-0.49876152,  1.87442714, -1.39061772, -1.04563275],\n",
       "       [-0.49876152,  1.4272477 , -1.27600637, -1.31260282],\n",
       "       [-0.37686461, -1.47941864, -0.01528151, -0.24472256],\n",
       "       [ 0.59831066, -0.58505976,  0.78699794,  0.4227026 ],\n",
       "       [ 0.72020757,  0.08570939,  1.01622064,  0.8231577 ],\n",
       "       [ 0.96400139, -0.13788033,  0.38585821,  0.28921757],\n",
       "       [ 1.69538284,  1.20365799,  1.3600547 ,  1.75755292],\n",
       "       [-0.13307079, -0.36147005,  0.27124686,  0.15573254],\n",
       "       [ 2.18297047, -0.13788033,  1.64658307,  1.22361279],\n",
       "       [-0.2549677 , -0.13788033,  0.44316389,  0.4227026 ],\n",
       "       [-0.86445224,  0.98006827, -1.33331205, -1.31260282],\n",
       "       [ 2.30486738, -0.58505976,  1.70388875,  1.09012776],\n",
       "       [-0.01117388, -0.80864948,  0.21394119, -0.24472256],\n",
       "       [-0.74255534,  0.75647855, -1.33331205, -1.31260282],\n",
       "       [-0.98634915,  0.98006827, -1.39061772, -1.17911778],\n",
       "       [-0.86445224,  1.65083742, -1.04678367, -1.04563275],\n",
       "       [-0.98634915, -2.37377751, -0.12989286, -0.24472256],\n",
       "       [ 0.59831066, -0.80864948,  0.67238659,  0.8231577 ],\n",
       "       [-1.23014297,  0.75647855, -1.04678367, -1.31260282],\n",
       "       [-0.98634915, -0.13788033, -1.2187007 , -1.31260282],\n",
       "       [-0.86445224,  0.53288883, -1.16139502, -0.91214772],\n",
       "       [-0.2549677 , -0.80864948,  0.27124686,  0.15573254],\n",
       "       [-0.86445224,  0.75647855, -1.27600637, -1.31260282],\n",
       "       [-0.13307079, -0.13788033,  0.27124686,  0.02224751],\n",
       "       [ 2.30486738,  1.65083742,  1.70388875,  1.35709783],\n",
       "       [-1.47393679,  0.30929911, -1.33331205, -1.31260282],\n",
       "       [ 0.47641375, -0.36147005,  0.32855254,  0.15573254],\n",
       "       [-0.13307079, -1.25582892,  0.72969227,  1.09012776],\n",
       "       [-0.37686461,  2.5451963 , -1.33331205, -1.31260282],\n",
       "       [ 0.23261993, -0.13788033,  0.61508092,  0.8231577 ],\n",
       "       [-0.01117388, -0.80864948,  0.78699794,  0.95664273],\n",
       "       [ 0.23261993, -1.92659808,  0.15663551, -0.24472256],\n",
       "       [-0.49876152, -0.13788033,  0.44316389,  0.4227026 ],\n",
       "       [ 0.47641375,  0.75647855,  0.95891497,  1.49058286],\n",
       "       [-0.37686461, -1.70300836,  0.15663551,  0.15573254],\n",
       "       [-0.49876152,  1.87442714, -1.16139502, -1.04563275],\n",
       "       [-0.98634915, -1.70300836, -0.24450422, -0.24472256],\n",
       "       [ 0.72020757, -0.80864948,  0.90160929,  0.95664273],\n",
       "       [-0.98634915,  0.53288883, -1.33331205, -1.31260282],\n",
       "       [-0.98634915,  0.30929911, -1.4479234 , -1.31260282],\n",
       "       [-0.37686461, -1.47941864,  0.04202416, -0.11123753],\n",
       "       [ 1.08589829, -0.13788033,  0.72969227,  0.68967267],\n",
       "       [-1.10824606,  0.08570939, -1.27600637, -1.31260282],\n",
       "       [-0.01117388, -0.58505976,  0.78699794,  1.62406789],\n",
       "       [-0.98634915,  0.75647855, -1.27600637, -1.31260282],\n",
       "       [-0.98634915,  0.98006827, -1.2187007 , -0.77866269],\n",
       "       [ 0.11072303,  0.30929911,  0.61508092,  0.8231577 ],\n",
       "       [-0.86445224, -1.25582892, -0.41642124, -0.11123753],\n",
       "       [ 1.32969211,  0.30929911,  1.130832  ,  1.49058286],\n",
       "       [ 0.23261993, -0.80864948,  0.78699794,  0.55618763],\n",
       "       [ 0.35451684, -1.0322392 ,  1.07352632,  0.28921757],\n",
       "       [ 2.30486738, -0.13788033,  1.3600547 ,  1.49058286],\n",
       "       [-0.37686461, -1.25582892,  0.15663551,  0.15573254],\n",
       "       [-1.7177306 , -0.36147005, -1.33331205, -1.31260282],\n",
       "       [-1.83962751, -0.13788033, -1.50522907, -1.44608785],\n",
       "       [ 0.23261993, -1.92659808,  0.72969227,  0.4227026 ],\n",
       "       [ 1.69538284,  0.30929911,  1.30274902,  0.8231577 ],\n",
       "       [-1.47393679,  0.08570939, -1.27600637, -1.31260282],\n",
       "       [-0.86445224,  0.98006827, -1.33331205, -1.17911778],\n",
       "       [-1.7177306 , -0.13788033, -1.39061772, -1.31260282],\n",
       "       [ 0.59831066, -1.25582892,  0.67238659,  0.4227026 ],\n",
       "       [ 0.59831066,  0.75647855,  1.07352632,  1.62406789],\n",
       "       [-1.47393679,  0.75647855, -1.33331205, -1.17911778],\n",
       "       [ 1.2077952 , -0.13788033,  1.01622064,  1.22361279],\n",
       "       [ 0.59831066,  0.53288883,  1.30274902,  1.75755292],\n",
       "       [-1.35203988,  0.30929911, -1.39061772, -1.31260282],\n",
       "       [ 0.35451684, -0.36147005,  0.55777524,  0.28921757],\n",
       "       [ 0.84210448, -0.58505976,  0.50046957,  0.4227026 ],\n",
       "       [ 0.47641375, -0.58505976,  0.61508092,  0.8231577 ],\n",
       "       [ 1.45158902,  0.30929911,  0.55777524,  0.28921757],\n",
       "       [ 0.72020757,  0.30929911,  0.90160929,  1.49058286],\n",
       "       [-0.86445224,  1.65083742, -1.2187007 , -1.31260282],\n",
       "       [ 1.32969211,  0.08570939,  0.95891497,  1.22361279],\n",
       "       [ 0.11072303, -0.13788033,  0.27124686,  0.4227026 ],\n",
       "       [ 0.84210448, -0.13788033,  0.84430362,  1.09012776],\n",
       "       [-0.13307079, -1.0322392 , -0.12989286, -0.24472256],\n",
       "       [-0.74255534, -0.80864948,  0.09932984,  0.28921757],\n",
       "       [ 0.35451684, -0.13788033,  0.50046957,  0.28921757],\n",
       "       [-1.5958337 , -1.70300836, -1.39061772, -1.17911778],\n",
       "       [ 0.96400139, -0.36147005,  0.50046957,  0.15573254],\n",
       "       [-0.37686461, -1.0322392 ,  0.38585821,  0.02224751],\n",
       "       [-0.62065843,  1.4272477 , -1.27600637, -1.31260282],\n",
       "       [-0.2549677 , -0.13788033,  0.21394119,  0.15573254],\n",
       "       [ 1.81727975, -0.36147005,  1.47466605,  0.8231577 ],\n",
       "       [ 1.08589829,  0.53288883,  1.130832  ,  1.22361279],\n",
       "       [-0.86445224,  1.4272477 , -1.27600637, -1.04563275],\n",
       "       [-1.10824606, -1.47941864, -0.24450422, -0.24472256],\n",
       "       [ 1.08589829,  0.53288883,  1.130832  ,  1.75755292],\n",
       "       [ 1.69538284, -0.13788033,  1.18813767,  0.55618763],\n",
       "       [-1.10824606,  1.20365799, -1.33331205, -1.44608785],\n",
       "       [ 1.08589829,  0.08570939,  1.07352632,  1.62406789],\n",
       "       [-1.10824606, -0.13788033, -1.33331205, -1.31260282],\n",
       "       [ 1.32969211,  0.08570939,  0.67238659,  0.4227026 ],\n",
       "       [ 1.93917666, -0.58505976,  1.3600547 ,  0.95664273],\n",
       "       [ 0.59831066, -0.36147005,  1.07352632,  0.8231577 ],\n",
       "       [-0.13307079, -0.58505976,  0.21394119,  0.15573254],\n",
       "       [ 0.84210448, -0.13788033,  1.01622064,  0.8231577 ],\n",
       "       [ 0.59831066, -1.70300836,  0.38585821,  0.15573254],\n",
       "       [ 0.72020757, -0.36147005,  0.32855254,  0.15573254],\n",
       "       [-0.2549677 , -0.58505976,  0.67238659,  1.09012776],\n",
       "       [ 0.11072303, -0.13788033,  0.78699794,  0.8231577 ],\n",
       "       [-0.49876152,  0.75647855, -1.16139502, -1.31260282],\n",
       "       [ 0.35451684, -0.58505976,  0.15663551,  0.15573254],\n",
       "       [-1.10824606, -1.25582892,  0.44316389,  0.68967267],\n",
       "       [-0.01117388,  2.09801686, -1.4479234 , -1.31260282],\n",
       "       [-0.01117388, -1.0322392 ,  0.15663551,  0.02224751],\n",
       "       [ 1.57348593, -0.13788033,  1.24544335,  1.22361279]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 归一化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、模型的选择与训练\n",
    "选择简单的机器学习模型：**KNN**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用KNN算法进行训练\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# 绘制损失函数曲线\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1, 31), [KNeighborsClassifier(n_neighbors=i).fit(X_train, y_train).score(X_test, y_test) for i in range(1, 31)])\n",
    "plt.xlabel('K值')\n",
    "plt.show()\n",
    "\n"
   ]
  }
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